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data-engineering-skills
von AltimateAI · AltimateAI/data-engineering-skills
9 Claude Code skills for analytics engineering: 7 dbt workflows + 2 Snowflake query optimizers. 53% pass on real dbt tasks, 84% on Snowflake tuning.
Skills for the daily grind of analytics engineering. dbt skills cover creating, debugging, testing, documenting, migrating, refactoring, and incremental models. Snowflake skills find expensive queries and optimize either by text or by query_id. Philosophy: 'Read before you write. Build after you write. Verify your output.'
Warum nutzen
Hauptfunktionen
7 dbt skills — create, debug, test, document, migrate, refactor, incremental 2 Snowflake skills — find expensive queries, optimize by text/id Skills auto-activate based on task context Benchmarked: 53% real-world dbt pass rate, 84% Snowflake optimization pass rate Includes dependency tracking and output verification steps
Live-Demo
In der Praxis
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Anwendungsfälle
Praxisnahe Nutzung: data-engineering-skills
Wann einsetzen: dbt run just failed with a cryptic error and you don't know if it's schema, lineage, or SQL.
Ablauf
Feed Claude the error + model
Use debugging-dbt-errors. Here's the stderr and models/marts/fct_orders.sql. Diagnose the root cause — don't guess.✓ Kopiert
→ Claude reads upstream refs, diagnoses in order: schema → lineage → SQL
Apply the fix and verify
Apply the fix and run dbt build --select fct_orders+. Show me the before/after row counts.✓ Kopiert
→ Clean run + row count verification
Ergebnis: Green CI plus a note of the root cause so it doesn't recur.
Fallstricke
Fixing a symptom downstream when the bug is upstream — The skill enforces an upstream-first diagnosis; don't skip the lineage step
Wann einsetzen: Finance flagged the Snowflake bill and you need to cut it without breaking dashboards.
Voraussetzungen
Snowflake role with ACCOUNT_USAGE access — ACCOUNTADMIN typically, or a dedicated cost role
Ablauf
Identify worst offenders
Use finding-expensive-queries to list the top 20 queries in the past 30 days by credit cost. Group by app/user.✓ Kopiert
→ Ranked table with credits, runtime, warehouse
Optimize each top one
For the top offender, use optimizing-query-by-id <query_id>. Propose rewrites with estimated savings.✓ Kopiert
→ Rewritten SQL + before/after explain plan
Validate and deploy
Run the rewrite in a test warehouse — confirm same row count and shape before we swap.✓ Kopiert
→ Safe swap candidate
Ergebnis: A prioritized list of fixes with measurable $ savings.
Fallstricke
Rewrites change row count silently — Always diff before deploying — the skill enforces this
Wann einsetzen: You've inherited a warehouse of nested CTEs and want them as documented, tested dbt models.
Ablauf
Point the skill at the source SQL
Use migrating-sql-to-dbt. Here's proc_monthly_revenue.sql. Convert it to dbt models with refs, documentation, and at least 2 tests per model.✓ Kopiert
→ One or more .sql files, schema.yml with docs and tests
Build and verify
dbt build the new models and compare row counts to the legacy output.✓ Kopiert
→ Row counts match within tolerance
Ergebnis: Legacy logic lives as testable dbt models.
Fallstricke
Hidden side effects in the proc (UPDATEs) — The skill flags side effects — separate them out, don't blindly convert
Wann einsetzen: A daily model has grown too big for full refresh.
Ablauf
Analyze the model
Use developing-incremental-models on models/events.sql. Pick a strategy (merge / insert_overwrite / delete+insert) and justify.✓ Kopiert
→ Strategy + unique_key + partition / cluster keys recommended
Implement and back-fill
Apply the incremental config; outline a safe back-fill plan.✓ Kopiert
→ Model + back-fill steps
Ergebnis: Daily runs that finish in minutes, not hours.
Fallstricke
unique_key gets duplicates on late data — Use merge and test it
Kombinationen
Mit anderen MCPs für 10-fache Wirkung
Apply the same optimize-by-id pattern to BigQuery expensive queries
Adapt finding-expensive-queries for BigQuery INFORMATION_SCHEMA.JOBS and list top 20.✓ Kopiert
data-engineering-skill +
github Open a PR per migrated model so each is reviewable
For every migrated model, open a GitHub PR with dbt test output attached.✓ Kopiert
Kosten & Limits
Was der Betrieb kostet
API-Kontingent Snowflake queries cost credits like any other — ACCOUNT_USAGE reads are cheap
Tokens pro Aufruf 5–15k per dbt skill invocation
Kosten in € Free skill
Tipp Run finding-expensive-queries once weekly, not on every session
Sicherheit
Rechte, Secrets, Reichweite
Minimale Scopes: dbt: read + write to your project Snowflake: ACCOUNT_USAGE for cost skills
Credential-Speicherung: dbt profiles.yml / Snowflake key-pair in env; the skill doesn't store secrets
Datenabfluss: None from the skill directly
Niemals gewähren: SYSADMIN to the Claude session unless absolutely needed
Fehlerbehebung
Häufige Fehler und Lösungen
dbt compile succeeds, run fails with column not foundStale lineage — dbt deps + dbt clean + dbt build --select model+
finding-expensive-queries returns nothingACCOUNT_USAGE has ~45min delay; also confirm role has SNOWFLAKE.ACCOUNT_USAGE
Prüfen: SHOW GRANTS TO ROLE <role>
Alternativen
data-engineering-skills vs. andere
Alternative Wann stattdessen Kompromiss
dbt Cloud IDE You prefer managed UI over terminal No Claude in the loop SQL query optimizers (Select.dev, etc.) You want visual query plans Separate tool, separate context